Data Science Out of The Box : Case Studies in the Telecommunications Industry

Telecommunications service providers (or telcos) have access to massive amounts of historical and streaming data about subscribers. However, it often takes them a long time to build, operationalize and gain value from various machine learning and analytic models. This is true even for relatively common use-cases like churn prediction, purchase propensity, next topup or purchase prediction, subscriber profiling, customer experience modeling, recommendation engines and fraud detection. In this talk, I shall describe our approach to tackling this problem, which involved having a pre-packaged set of analytic pipelines on a scalable Big Data architecture that work on several standard and well known telco data formats and sources, and that we were able to reuse across several different telcos. This allows the telcos to deploy the analytic pipelines on their data, out of the box, and go live in a matter of weeks, as opposed to the several months it used to take if they started from scratch. In the talk, I shall describe our experiences in deploying the pre-packaged analytic pipelines with several telcos in North America, South East Asia and the Middle East. The pipelines work on a variety of historical and streaming data, including call data records having voice, SMS and data usage information, purchase and recharge behavior, location information, browsing/clickstream data, billing and payment information, smartphone device logs, etc. The pipelines run on a combination of Spark and Unscrambl BRAIN, which includes a real-time machine learning framework, a scalable profile store based on Redis and an “aggregation engine” that stores efficient summaries of time-series data. I shall describe some of the machine learning models that get trained and scored as part of these pipelines. I shall also remark on how reusable certain models are across different telcos, and how a similar set of features can be used for models like next topup or purchase prediction, churn prediction and purchase propensity across similar telcos in different geographies.

Anand Ranganathan is a co-founder and the Chief AI Officer at Unscrambl, Inc. He is a data scientist, AI engineer, Big Data developer, architect and researcher rolled into one person. He is leading Unscrambl's product development in several cutting-edge areas, including natural language processing, conversational analytics, automated insights, real-time optimization and decision-making, and automated marketing optimization. He has worked with numerous customers worldwide to design, implement and deploy Big Data, Stream Processing and AI-based solutions. Before joining Unscrambl, he was a Global Technical Ambassador, Master Inventor and Research Scientist at IBM. He received his PhD in Computer Science from University of Illinois Urbana-Champaign, and his BTech from the Indian Institute of Technology Madras. He also has over 70 academic journal and conference publications and 30 patent filings in his name.